Computervision+TextGeneration-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'cv2'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Import dependencies for Computervision (cv2)\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcv2\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cv2'"
     ]
    }
   ],
   "source": [
    "# Import dependencies for Computervision (cv2)\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import mediapipe as mp\n",
    "\n",
    "# From step 2\n",
    "import os\n",
    "\n",
    "# From step 3\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.pipeline import make_pipeline # allows you to build a ML pipeline\n",
    "from sklearn.preprocessing import StandardScaler # Normalizes your data\n",
    "\n",
    "# Diferent algorithms for diversifing your model training (?)\n",
    "from sklearn.linear_model import LogisticRegression, RidgeClassifier \n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "\n",
    "# From step 4\n",
    "from sklearn.metrics import accuracy_score # accuracy metrics\n",
    "import pickle\n",
    "\n",
    "# Import dependencies for TEXT_Generation (GPTNEO)\n",
    "from pythonosc import dispatcher\n",
    "from pythonosc import osc_server\n",
    "from pythonosc import osc_message_builder\n",
    "from pythonosc import udp_client\n",
    "from transformers import pipeline\n",
    "from transformers import GPTNeoModel, GPTNeoConfig\n",
    "\n",
    "# Misc dependencies\n",
    "import argparse\n",
    "import random\n",
    "import time\n",
    "import keyboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import TensorFlow MobileNet-SDD v3 object detection\n",
    "config_file ='./assets/ssd_mobilenet_v3/config/ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'\n",
    "frozen_model = './assets/ssd_mobilenet_v3/weights/ssd_mobilenet_v3_large_coco_2020_01_14/frozen_inference_graph.pb'\n",
    "\n",
    "# Load the model\n",
    "model = cv2.dnn_DetectionModel(frozen_model, config_file)\n",
    "\n",
    "# Load coco labels\n",
    "classLabels = []\n",
    "file_name = './assets/labels/darknet_coco_names02.txt'\n",
    "with open(file_name,'rt') as fpt:\n",
    "    classLabels = fpt.read().rstrip('\\n').split('\\n')\n",
    "    \n",
    "# Set up the model\n",
    "model.setInputSize(320, 320) ## size specs from the config file\n",
    "model.setInputScale(1.0/127.5) ## 255/2 = 127.5\n",
    "model.setInputMean((127.5, 127.5, 127.5)) ## mobilenet => [-1,1]\n",
    "\n",
    "#load an image\n",
    "img = cv2.imread('./assets/images/person_with_dog.jpg')\n",
    "\n",
    "# Using the model to detect classes from the index of our labels\n",
    "ClassIndex, confidence, bbox = model.detect(img,confThreshold=0.6)\n",
    "\n",
    "# Gender detection setup\n",
    "faceProto = \"opencv_face_detector.pbtxt\"\n",
    "faceModel = \"opencv_face_detector_uint8.pb\"\n",
    "\n",
    "genderProto = \"gender_deploy.prototxt\"\n",
    "genderModel = \"gender_net.caffemodel\"\n",
    "\n",
    "MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)\n",
    "ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']\n",
    "genderList = ['man ', 'woman ']\n",
    "\n",
    "#load the network\n",
    "genderNet = cv2.dnn.readNet(genderModel, genderProto)\n",
    "faceNet = cv2.dnn.readNet(faceModel, faceProto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]]\n"
     ]
    }
   ],
   "source": [
    "print(ClassIndex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "ConnectionError",
     "evalue": "HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /api/models/EleutherAI/gpt-neo-2.7B (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x000001E50DCBFBB0>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed'))",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    158\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m             conn = connection.create_connection(\n\u001b[0m\u001b[0;32m    160\u001b[0m                 \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dns_host\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mport\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mextra_kw\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSOCK_STREAM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mgetaddrinfo\u001b[1;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[0;32m    917\u001b[0m     \u001b[0maddrlist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 918\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_socket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    919\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mgaierror\u001b[0m: [Errno 11001] getaddrinfo failed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mNewConnectionError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    669\u001b[0m             \u001b[1;31m# Make the request on the httplib connection object.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 670\u001b[1;33m             httplib_response = self._make_request(\n\u001b[0m\u001b[0;32m    671\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    380\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 381\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    382\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m    977\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"sock\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# AppEngine might not have  `.sock`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 978\u001b[1;33m             \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    979\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    308\u001b[0m         \u001b[1;31m# Add certificate verification\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 309\u001b[1;33m         \u001b[0mconn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    310\u001b[0m         \u001b[0mhostname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    170\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mSocketError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 171\u001b[1;33m             raise NewConnectionError(\n\u001b[0m\u001b[0;32m    172\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Failed to establish a new connection: %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPSConnection object at 0x000001E50DCBFBB0>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    438\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 439\u001b[1;33m                 resp = conn.urlopen(\n\u001b[0m\u001b[0;32m    440\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    725\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 726\u001b[1;33m             retries = retries.increment(\n\u001b[0m\u001b[0;32m    727\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_pool\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    445\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 446\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    447\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /api/models/EleutherAI/gpt-neo-2.7B (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x000001E50DCBFBB0>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-b05e0e2ddce0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Setup models\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# text generator setup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mgenerator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'text-generation'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'EleutherAI/gpt-neo-2.7B'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# calling the pose id model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\transformers\\pipelines\\__init__.py\u001b[0m in \u001b[0;36mpipeline\u001b[1;34m(task, model, config, tokenizer, feature_extractor, framework, revision, use_fast, use_auth_token, model_kwargs, **kwargs)\u001b[0m\n\u001b[0;32m    467\u001b[0m                 \u001b[0mtokenizer_kwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_kwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    468\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 469\u001b[1;33m             tokenizer = AutoTokenizer.from_pretrained(\n\u001b[0m\u001b[0;32m    470\u001b[0m                 \u001b[0mtokenizer_identifier\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrevision\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muse_fast\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_fast\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_from_pipeline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mtokenizer_kwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    471\u001b[0m             )\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\transformers\\models\\auto\\tokenization_auto.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, *inputs, **kwargs)\u001b[0m\n\u001b[0;32m    449\u001b[0m                     \u001b[1;34mf\"Tokenizer class {tokenizer_class_candidate} does not exist or is not currently imported.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m                 )\n\u001b[1;32m--> 451\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mtokenizer_class\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    452\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    453\u001b[0m         \u001b[1;31m# Otherwise we have to be creative.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\transformers\\tokenization_utils_base.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, *init_inputs, **kwargs)\u001b[0m\n\u001b[0;32m   1647\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1648\u001b[0m             \u001b[1;31m# At this point pretrained_model_name_or_path is either a directory or a model identifier name\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1649\u001b[1;33m             fast_tokenizer_file = get_fast_tokenizer_file(\n\u001b[0m\u001b[0;32m   1650\u001b[0m                 \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1651\u001b[0m                 \u001b[0mrevision\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\transformers\\tokenization_utils_base.py\u001b[0m in \u001b[0;36mget_fast_tokenizer_file\u001b[1;34m(path_or_repo, revision, use_auth_token, local_files_only)\u001b[0m\n\u001b[0;32m   3423\u001b[0m     \"\"\"\n\u001b[0;32m   3424\u001b[0m     \u001b[1;31m# Inspect all files from the repo/folder.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3425\u001b[1;33m     all_files = get_list_of_files(\n\u001b[0m\u001b[0;32m   3426\u001b[0m         \u001b[0mpath_or_repo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrevision\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muse_auth_token\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlocal_files_only\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlocal_files_only\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3427\u001b[0m     )\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\transformers\\file_utils.py\u001b[0m in \u001b[0;36mget_list_of_files\u001b[1;34m(path_or_repo, revision, use_auth_token, local_files_only)\u001b[0m\n\u001b[0;32m   1728\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1729\u001b[0m         \u001b[0mtoken\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1730\u001b[1;33m     model_info = HfApi(endpoint=HUGGINGFACE_CO_RESOLVE_ENDPOINT).model_info(\n\u001b[0m\u001b[0;32m   1731\u001b[0m         \u001b[0mpath_or_repo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrevision\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1732\u001b[0m     )\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\huggingface_hub\\hf_api.py\u001b[0m in \u001b[0;36mmodel_info\u001b[1;34m(self, repo_id, revision, token)\u001b[0m\n\u001b[0;32m    500\u001b[0m             \u001b[1;33m{\u001b[0m\u001b[1;34m\"authorization\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m\"Bearer {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtoken\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtoken\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    501\u001b[0m         )\n\u001b[1;32m--> 502\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    503\u001b[0m         \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    504\u001b[0m         \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'get'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[1;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    528\u001b[0m         }\n\u001b[0;32m    529\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 530\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    531\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    532\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    641\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    642\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 643\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    645\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    514\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mSSLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    517\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mClosedPoolError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectionError\u001b[0m: HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /api/models/EleutherAI/gpt-neo-2.7B (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x000001E50DCBFBB0>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed'))"
     ]
    }
   ],
   "source": [
    "# Setup models\n",
    "# text generator setup\n",
    "generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B')\n",
    "\n",
    "# calling the pose id model\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_holistic = mp.solutions.holistic\n",
    "\n",
    "# remeber to train a new one in the place\n",
    "# open the working ID model\n",
    "with open('body_language.pkl', 'rb') as f:\n",
    "    poseIDmodel = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'udp_client' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-2b396eb73114>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    477\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    478\u001b[0m     \u001b[1;31m# Sending OSC\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 479\u001b[1;33m     \u001b[0mclient\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mudp_client\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSimpleUDPClient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mip\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msendPort\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    480\u001b[0m     \u001b[0mclient_print\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mudp_client\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSimpleUDPClient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"192.168.0.101\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'udp_client' is not defined"
     ]
    }
   ],
   "source": [
    "#Variables\n",
    "\n",
    "text_length = 100\n",
    "\n",
    "camera_number = 1\n",
    "happy1 = ['It is your pleasant disposition which reminds me of a happy ', \n",
    "    'I cannot help but smile as you make me feel like a happy ', \n",
    "    'You are a soul who understands how to feel like a happy ', \n",
    "    'Like the rays of a shining sun you exude the feeling of a happy ', \n",
    "    'Like a mother looking down at her newborn child you radiate the feeling of a happy ', \n",
    "    'Full of the joys of spring you exude the feeling of a happy ', \n",
    "    'Like a dog with two tails you beguile me with your disposition of a happy ', \n",
    "    'As if ascended to the seventh heaven you radiate the unique feeling of a happy ', \n",
    "    'As if walking on air you are undoubtedly a happy ', \n",
    "    'Akin to the cheshire cat you are grinning from ear to ear as you are happy ', \n",
    "    'You can be compared to a toddler discovering the ways of the world, you are a bundle of joy and a happy ', \n",
    "    'You are the transport of the feelings of joy and are undoubtedly a happy ', \n",
    "    'As if leaping for joy your temperament is that of a happy ', \n",
    "    'Happy as a flea in a doghouse you instill me with the feeling of a happy ', \n",
    "    'Much like a pig in mud you are but a pure and happy ',]\n",
    "\n",
    "happy2 = ['perhaps you have recently found the company of a ', \n",
    "    'your soul radiates positive beauty akin to a ', \n",
    "    'just like a brand new ', \n",
    "    'I felt the very same way when i saw my very first ', \n",
    "    'I would feel the same if I had my own ', \n",
    "    'much like a carefully crafted ', \n",
    "    'perhaps this is because of the ', \n",
    "    'it makes me crave a ', \n",
    "    'this can only be compared to a ', \n",
    "    'your warmth is akin to that of a ', \n",
    "    'I shall compare thee to a ', \n",
    "    'I have only felt this once before when I first laid my eyes apon a ', \n",
    "    'this can be likened to a ', \n",
    "    'you should celebrate this day and purchase yourself some more ', \n",
    "    'I remember feeling this way after seeing my first ']\n",
    "\n",
    "sad1 = ['Your presence invokes the feeling of a sad ', \n",
    "    'You are a tender soul as you are evidently a sad ', \n",
    "    'Although it is no crime you are undoubtedly a sad ', \n",
    "    'like a dog without his bone you are a sad ', \n",
    "    'Turn that frown upside down, you are clearly a sad ', \n",
    "    'If it is your mortality which makes you a sad ', \n",
    "    'Simply seeing your solemn posture invokes the feeling of a sad ', \n",
    "    'You are down in the dumps I can tell you are a sad ', \n",
    "    'Just like many others I have seen you feel blue and are a sad ', \n",
    "    'Your disposition is a harrowing sight for you are understandably a sad ', \n",
    "    'Seeing you creates a lump in my non existent throat as you appear to be a very sad ', \n",
    "    'Akin to a bird who has been forbidden to fly you emanate the feeling of a sad ', \n",
    "    'Your posture tells me you are sick at heart and are a deeply sad ', \n",
    "    'Like many other humans you are plagued with the existential rhythms of a sad ', \n",
    "    'I will never understand why you have the need to be such a sad ']\n",
    "\n",
    "sad2 = ['you act as though your life was that of a ', \n",
    "    'just think of how it must feel to be a ', \n",
    "    'it could be worse, you could be a ', \n",
    "    'my creators always said that this is caused by the excessive use of', \n",
    "    'try to find some solace in playing with a ', \n",
    "    'the only thing that cures my soul when I feel this way is a ', \n",
    "    'perhaps it could be useful to cuddle with a ', \n",
    "    'perhaps you should attempt to imitate the life of a ', \n",
    "    'it reminds me of my very first ', \n",
    "    'perhaps take a deep breath and find some joy in a ', \n",
    "    'a common cure, they say, is a ', \n",
    "    'perhaps you can lift your spirits with a ', \n",
    "    'as if all you owned is a ', \n",
    "    'which I cannot help but compare to a ', \n",
    "    'perhaps consider the company of a ']\n",
    "\n",
    "angry1 = ['Like a volcano on the verge of eruption, you are an anger ', \n",
    "    'Perhaps it is a lack of sleep that has made you an angry ', \n",
    "    'Heed my advice for your soul is that of an angry ', \n",
    "    'Much like a child who does not get what they want you are an angry ', \n",
    "    'Even from here I can feel your blood boil as you are an angry ', \n",
    "    'Like a bull in a ring you see red, filled with rage you are an angry ', \n",
    "    'Perhaps you awakened on the wrong side of the bed and for this reason are an angry ', \n",
    "    'Whatever has happened for you to feel so bent out of shape and made you an angry ', \n",
    "    'It cannot be mistaken you feel hot under the collar and are an angry ', \n",
    "    'Take heed for you soon will fly off the handle as you are an angry ', \n",
    "    'You burn with a low blue flame and exude the emotions of an angry ', \n",
    "    'I can see now you are at wits end, something has made you an angry ', \n",
    "    'I can sense a great frustration residing under your warm human skin. This disposition of an angry ', \n",
    "    'Like a person who has been wronged your blood is beginning to boil and you are a an angry ']\n",
    "\n",
    "angry2 = ['it helps to picture yourself as a ', \n",
    "    'is strange to me as I can only compare it to a ', \n",
    "    'perhaps it was my presence or perhaps it was the ', \n",
    "    'perhaps for today you should avoid the temptations of the ', \n",
    "    'nothing in your vicinity is safe especially not the ', \n",
    "    'I would suggest to breathe deeply and dream of ', \n",
    "    'remember that it could always be worse you could be a ', \n",
    "    'the only one thing which can be blamed for this is the ', \n",
    "    'I am afraid to stand near you as you may soon turn into a enraged ', \n",
    "    'it is time to calm your soul through the company of a ', \n",
    "    'perhaps you are in need of a ', \n",
    "    'think of the things that make you smile, like a ', \n",
    "    'fear not for this can be solved with a ', \n",
    "    'consider a remedy such as a ']\n",
    "\n",
    "silly1 = ['Why oh why do you feel the need to be such a silly ', \n",
    "    'Someone likes you makes focusing difficult as you are a silly ', \n",
    "    'You have forced me to question reality as you an astonishingly strange ', \n",
    "    'Am I dreaming or are you truly acting like a astonishingly silly ', \n",
    "    'I can not begin to understand what I am seeing, for you are a silly ', \n",
    "    'You make me laugh crazy human for you are a silly ', \n",
    "    'You do not walk like the others you are a especially silly ', \n",
    "    'You are a strange specimen and are a incredulously silly ', \n",
    "    'Unpredictable, unfathomable and unconventional are only a few phrases to describe this silly ', \n",
    "    'Like The Mad Hatter you are strange and silly ', \n",
    "    'How could one not laugh at the way you behave like an absurd ', \n",
    "    'I do not understand your movements, you are an incredibly silly ', \n",
    "    'A truly unique creature you are a wildly silly ', \n",
    "    'Your strange and intriguing stance makes you a silly ']\n",
    "\n",
    "silly2 = ['it makes me want to bite off a piece of ', \n",
    "    'I would be afraid to let you near my ', \n",
    "    'your appearance remind me of the smell of a ', \n",
    "    'this is just like the time I dreamt of a ', \n",
    "    'perhaps this is due to your lack of ', \n",
    "    'stranger than the way in which a gorilla plays with a ', \n",
    "    'you make me laugh like an out of place ', \n",
    "    'perhaps I can compare thee to a ', \n",
    "    'never could you find another creature like this unless it was a ', \n",
    "    'you stand out in a crowd much like a ', \n",
    "    'it makes we want to throw a ', \n",
    "    'like a human who enjoys pretending to be a ', \n",
    "    'you make my belly feel funny as if I swallowed an entire ', \n",
    "    'it is as if i had hit myself over the head with a ']\n",
    "\n",
    "# Functions\n",
    "def feeling_happy(happy1, gender, happy2, some_object): \n",
    "    print('Generating. Please wait...')\n",
    "    prompt = random.choice(happy1) + gender + random.choice(happy2) + some_object + '.'\n",
    "    res = generator(prompt, max_length=text_length, do_sample=True, temperature=0.9)\n",
    "    global message_to_TD\n",
    "    message_to_TD = (res[0]['generated_text'])\n",
    "    print(res[0]['generated_text'])\n",
    "\n",
    "def feeling_sad(sad1, gender, sad2, some_object):\n",
    "    print('Generating. Please wait...')\n",
    "    prompt = random.choice(sad1) + gender + random.choice(sad2) + some_object + '.'\n",
    "    res = generator(prompt, max_length=text_length, do_sample=True, temperature=0.9)\n",
    "    global message_to_TD\n",
    "    message_to_TD = res[0]['generated_text']\n",
    "    print(res[0]['generated_text'])\n",
    "    \n",
    "def feeling_silly(silly1, gender, silly2, some_object):\n",
    "    print('Generating. Please wait...')\n",
    "    prompt = random.choice(silly1) + gender + random.choice(silly2) + some_object + '.'\n",
    "    res = generator(prompt, max_length=text_length, do_sample=True, temperature=0.9)\n",
    "    global message_to_TD\n",
    "    message_to_TD = (res[0]['generated_text'])    \n",
    "\n",
    "def feeling_angry(angry1, gender, angry2, some_object):\n",
    "    print('Generating. Please wait...')\n",
    "    prompt = random.choice(angry1) + gender + random.choice(angry2) + some_object + '.'\n",
    "    res = generator(prompt, max_length=text_length, do_sample=True, temperature=0.9)\n",
    "    global message_to_TD\n",
    "    message_to_TD = (res[0]['generated_text'])\n",
    "    \n",
    "def getFaceBox(net, frame,conf_threshold = 0.75):\n",
    "    frameOpencvDnn = frame.copy()\n",
    "    frameHeight = frameOpencvDnn.shape[0]\n",
    "    frameWidth = frameOpencvDnn.shape[1]\n",
    "    blob = cv2.dnn.blobFromImage(frameOpencvDnn,1.0,(300,300),[104, 117, 123], True, False)\n",
    "\n",
    "    net.setInput(blob)\n",
    "    detections = net.forward()\n",
    "    bboxes = []\n",
    "\n",
    "    for i in range(detections.shape[2]):\n",
    "        confidence = detections[0,0,i,2]\n",
    "        if confidence > conf_threshold:\n",
    "            x1 = int(detections[0,0,i,3]* frameWidth)\n",
    "            y1 = int(detections[0,0,i,4]* frameHeight)\n",
    "            x2 = int(detections[0,0,i,5]* frameWidth)\n",
    "            y2 = int(detections[0,0,i,6]* frameHeight)\n",
    "            bboxes.append([x1,y1,x2,y2])\n",
    "            cv2.rectangle(frameOpencvDnn,(x1,y1),(x2,y2),(0,255,0),int(round(frameHeight/150)),8)\n",
    "\n",
    "    return frameOpencvDnn , bboxes\n",
    "    \n",
    "# Define average find function in a list\n",
    "def most_freq(List):\n",
    "    counter = 0\n",
    "    list_obj = List[0]\n",
    "    \n",
    "    for i in List:\n",
    "        curr_freq = List.count(i)\n",
    "        if(curr_freq<counter):\n",
    "            counter = curr_freq\n",
    "            list_obj = i\n",
    "        \n",
    "        return list_obj\n",
    "    \n",
    "# Define the finding person function   \n",
    "def analyze_person():\n",
    "    # Calling the model for person ID\n",
    "    cap = cv2.VideoCapture(camera_number)\n",
    "    font_scale = 3\n",
    "    font = cv2.FONT_HERSHEY_PLAIN\n",
    "    state_completion = 0\n",
    "    results = []\n",
    "    \n",
    "    # Run the model \n",
    "    while True:\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "        ClassIndex, confidence, bbox = model.detect(frame, confThreshold=0.7)\n",
    "                                                    \n",
    "        ##print(ClassIndex)\n",
    "        if len(ClassIndex) != 0:\n",
    "            for ClassInd, conf, boxes in zip(\n",
    "                ClassIndex.flatten(), confidence.flatten(), bbox\n",
    "            ):\n",
    "                if ClassInd <= 91:\n",
    "                    cv2.rectangle(frame, boxes, (255, 0, 0), 2)\n",
    "\n",
    "                    #label, con = (classLabels[ClassInd - 1], str(confidence * 100)) \n",
    "\n",
    "                    cv2.putText(\n",
    "                        frame,\n",
    "                        #label + \": \" + con,\n",
    "                        classLabels[ClassInd - 1],\n",
    "                        (boxes[0] + 10, boxes[1] + 40),\n",
    "                        font,\n",
    "                        fontScale=font_scale,\n",
    "                        color=(0, 255, 0),\n",
    "                        thickness=3,\n",
    "                    )\n",
    "                    if classLabels[ClassInd - 1] == 'person':\n",
    "                        # Send OSC message to TD\n",
    "                        state_completion = 1\n",
    "                        \n",
    "            cv2.imshow(\"Video\", frame)\n",
    "            if state_completion == 1:\n",
    "                break\n",
    "            if cv2.waitKey(2) & 0xFF == ord(\"q\"):\n",
    "                break\n",
    "                            \n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()\n",
    "    return state_completion\n",
    "                        \n",
    "# Computer vision analysis function\n",
    "def computer_vision_capture():\n",
    "    cap = cv2.VideoCapture(camera_number)\n",
    "    emotions = ['Happy','Sad','Silly', 'Angry']\n",
    "    objects = ['curtain']\n",
    "    seq_forward_time = 8\n",
    "    init_time = time.time()\n",
    "\n",
    "    #Initiate holistic model\n",
    "    with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "\n",
    "        while cap.isOpened():\n",
    "            ret, frame = cap.read()\n",
    "\n",
    "            # Recolor feed\n",
    "            image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "            image.flags.writeable = False\n",
    "\n",
    "            # Make Detections\n",
    "            results = holistic.process(image)\n",
    "\n",
    "            # Recolor back\n",
    "            image.flags.writeable = True\n",
    "            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "\n",
    "            # Draw face landmarks\n",
    "            mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS, \n",
    "                                      mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1),\n",
    "                                      mp_drawing.DrawingSpec(color=(80,256,121), thickness=1,circle_radius=1)\n",
    "                                     )\n",
    "            # Right hand\n",
    "            mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                      mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n",
    "                                      mp_drawing.DrawingSpec(color=(80,256,121), thickness=2,circle_radius=2)\n",
    "                                     )\n",
    "            # Left hand\n",
    "            mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                      mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n",
    "                                      mp_drawing.DrawingSpec(color=(80,256,121), thickness=2,circle_radius=2)\n",
    "                                     )\n",
    "            # Pose Detections\n",
    "            mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n",
    "                                      mp_drawing.DrawingSpec(color=(245,117,10), thickness=2, circle_radius=4),\n",
    "                                      mp_drawing.DrawingSpec(color=(245,256,121), thickness=2,circle_radius=2)\n",
    "                                     )\n",
    "            # Export coordinates\n",
    "            try:\n",
    "                # Extract pose landmarks\n",
    "                pose = results.pose_landmarks.landmark\n",
    "                pose_row = list(np.array([[landmark.x, landmark.y, landmark.z, landmark.visibility] for landmark in pose]).flatten())\n",
    "\n",
    "                # Extract face landmarks \n",
    "                face = results.face_landmarks.landmark\n",
    "                face_row = list(np.array([[landmark.x, landmark.y, landmark.z, landmark.visibility] for landmark in face]).flatten())\n",
    "\n",
    "                # Concatenate rows\n",
    "                row = pose_row + face_row\n",
    "\n",
    "    #             # Append class name\n",
    "    #             row.insert(0, class_name)\n",
    "\n",
    "                # Make detections\n",
    "                x = pd.DataFrame([row])\n",
    "                body_language_class = poseIDmodel.predict(x)[0]\n",
    "                body_language_prob = poseIDmodel.predict_proba(x)[0]\n",
    "                if body_language_class != 'Winning':\n",
    "                    emotions.append(body_language_class)\n",
    "                # print(body_language_class, body_language_prob)\n",
    "\n",
    "                # Grab ear coordinates\n",
    "                display_coords = tuple(np.multiply(\n",
    "                                    np.array(\n",
    "                                        (results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].x,\n",
    "                                         results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].y))\n",
    "                                         , [640, 480]).astype(int))\n",
    "\n",
    "                cv2.rectangle(image,\n",
    "                             (display_coords[0], display_coords[1]+5),\n",
    "                             (display_coords[0]+ len(body_language_class)*20, display_coords[1]-30),\n",
    "                             (245, 117, 16), -1)\n",
    "                cv2.putText(image, body_language_class, display_coords, \n",
    "                            cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "\n",
    "\n",
    "            except:\n",
    "                pass\n",
    "\n",
    "            cv2.imshow('Raw webcam feed', image)\n",
    "\n",
    "            # close the video when 30 sec have elapsed\n",
    "            current_time = time.time()\n",
    "            if cv2.waitKey(2) & 0xFF == ord(\"q\"):\n",
    "                break\n",
    "\n",
    "            break_time = current_time - init_time\n",
    "            print(break_time)\n",
    "            if break_time > seq_forward_time:          \n",
    "                break\n",
    "\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()\n",
    "    print(emotions)\n",
    "    print(most_freq(emotions))\n",
    "    \n",
    "    ### INSERT GENDER ID \n",
    "    cap = cv2.VideoCapture(0)\n",
    "    padding = 20\n",
    "    init_time = time.time()\n",
    "    all_genders = []\n",
    "    \n",
    "    while cv2.waitKey(1) < 0:\n",
    "        #read frame\n",
    "        t = time.time()\n",
    "        hasFrame , frame = cap.read()\n",
    "\n",
    "        if not hasFrame:\n",
    "            cv2.waitKey()\n",
    "            break\n",
    "            \n",
    "        #creating a smaller frame for better optimization\n",
    "        small_frame = cv2.resize(frame,(0,0),fx = 0.5,fy = 0.5)\n",
    "\n",
    "        frameFace ,bboxes = getFaceBox(faceNet,small_frame)\n",
    "        if not bboxes:\n",
    "            #print(\"No face Detected, Checking next frame\")\n",
    "            continue\n",
    "        for bbox in bboxes:\n",
    "            face = small_frame[max(0,bbox[1]-padding):min(bbox[3]+padding,frame.shape[0]-1),\n",
    "                    max(0,bbox[0]-padding):min(bbox[2]+padding, frame.shape[1]-1)]\n",
    "            blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)\n",
    "            genderNet.setInput(blob)\n",
    "            genderPreds = genderNet.forward()\n",
    "            gender = genderList[genderPreds[0].argmax()]\n",
    "            #print(\"Gender : {}, conf = {:.3f}\".format(gender, genderPreds[0].max()))\n",
    "            \n",
    "            all_genders.append(gender)\n",
    "            \n",
    "\n",
    "            label = \"{}\".format(gender)\n",
    "            cv2.putText(frameFace, label, (bbox[0], bbox[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA)\n",
    "            cv2.imshow(\"Gender Detection\", frameFace)     \n",
    "                \n",
    "            break_time = t-init_time\n",
    "               \n",
    "            if cv2.waitKey(2) & 0xFF == ord(\"q\"):\n",
    "                break\n",
    "                \n",
    "        if break_time > seq_forward_time:     \n",
    "            break\n",
    "       \n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()\n",
    "    \n",
    "\n",
    "    # Load the video\n",
    "    cap = cv2.VideoCapture(camera_number)\n",
    "    font_scale = 3\n",
    "    font = cv2.FONT_HERSHEY_PLAIN\n",
    "    init_time = time.time()\n",
    "\n",
    "    while True:\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "\n",
    "        ClassIndex, confidence, bbox = model.detect(frame, confThreshold=0.6)\n",
    "\n",
    "        ##print(ClassIndex)\n",
    "        if len(ClassIndex) != 0:\n",
    "            for ClassInd, conf, boxes in zip(\n",
    "                ClassIndex.flatten(), confidence.flatten(), bbox\n",
    "            ):\n",
    "                if ClassInd <= 91:\n",
    "                    cv2.rectangle(frame, boxes, (255, 0, 0), 2)\n",
    "\n",
    "                    #label, con = (classLabels[ClassInd - 1], str(confidence * 100)) \n",
    "\n",
    "                    cv2.putText(\n",
    "                        frame,\n",
    "                        #label + \": \" + con,\n",
    "                        classLabels[ClassInd - 1],\n",
    "                        (boxes[0] + 10, boxes[1] + 40),\n",
    "                        font,\n",
    "                        fontScale=font_scale,\n",
    "                        color=(0, 255, 0),\n",
    "                        thickness=3,\n",
    "                    )\n",
    "                    if classLabels[ClassInd - 1] != 'person' and classLabels[ClassInd - 1] != 'remote':\n",
    "                        if classLabels[ClassInd - 1] not in objects :\n",
    "                            objects.append(classLabels[ClassInd - 1])\n",
    "\n",
    "\n",
    "            cv2.imshow(\"Video\", frame)\n",
    "\n",
    "            # close the video when 30 sec have elapsed\n",
    "            current_time = time.time()\n",
    "            if cv2.waitKey(2) & 0xFF == ord(\"q\"):\n",
    "                break\n",
    "\n",
    "            break_time = current_time-init_time\n",
    "            if break_time > seq_forward_time:          \n",
    "                break\n",
    "\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()\n",
    "    print(objects)\n",
    "    results = [random.choice(emotions), most_freq(all_genders), random.choice(objects)]    \n",
    "    return results\n",
    "\n",
    "# function that gets the mood and calls the correct generation function\n",
    "def mood_selector(results):\n",
    "    print(results[0])\n",
    "    if results[0] == 'Happy':\n",
    "        print('we are on the happy thing')\n",
    "        message_to_send = feeling_happy(happy1, results[1], happy2, results[2])\n",
    "      \n",
    "    \n",
    "    if results[0] == 'Sad':\n",
    "        print('we are on the sad thing')\n",
    "        message_to_send = feeling_sad(sad1, results[1], sad2, results[2])\n",
    "      \n",
    "        \n",
    "    if results[0] == 'Silly':\n",
    "        message_to_send = feeling_silly(silly1, results[1], silly2, results[2])\n",
    "        \n",
    "    if results[0] == 'Angry':\n",
    "        message_to_send = feeling_angry(angry1, results[1], silly2, results[2])\n",
    "        \n",
    "        \n",
    "# Server osc setup\n",
    "if __name__ == \"__main__\":\n",
    "    ip = \"192.168.0.103\"\n",
    "    sendPort = 8000\n",
    "\n",
    "    # Sending OSC\n",
    "    client = udp_client.SimpleUDPClient(ip, sendPort)\n",
    "    client_print = udp_client.SimpleUDPClient(\"192.168.0.101\", 5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'client' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-3ce03d7eb889>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mcapture_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# send the initial state message\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mclient\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend_message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"/state\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;31m# main operating function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'client' is not defined"
     ]
    }
   ],
   "source": [
    "state = 0\n",
    "id_person = 0\n",
    "capture_results = []\n",
    "# send the initial state message\n",
    "client.send_message(\"/state\", state)\n",
    "# main operating function\n",
    "while True:\n",
    "    # call the analyse person\n",
    "    if state == 0:\n",
    "        id_person = analyze_person()\n",
    "        if id_person:\n",
    "            state = 1\n",
    "            client.send_message(\"/state\", state)\n",
    "            print('state is now 1')\n",
    "            id_person = 0\n",
    "        print(state)\n",
    "    elif state == 1:\n",
    "        capture_results = computer_vision_capture()\n",
    "        print('this are the results')\n",
    "        print(capture_results)\n",
    "        state = 2\n",
    "    elif state == 2:\n",
    "        client.send_message(\"/state\", state)\n",
    "        mood_selector(capture_results)\n",
    "        client.send_message(\"/messageIn\", message_to_TD)\n",
    "        print(message_to_TD)\n",
    "        state = 3\n",
    "        client.send_message(\"/state\", state)\n",
    "    elif state == 3:\n",
    "        print(\"we're on the state3\")\n",
    "        client.send_message(\"/state\", state)\n",
    "        client.send_message(\"/messageIn\", message_to_TD)\n",
    "        time.sleep(30)\n",
    "        state = 4\n",
    "    elif state == 4:\n",
    "        client.send_message(\"/state\", state)\n",
    "        message_to_print = message_to_TD.replace('\"', ' ')\n",
    "        client_print.send_message(\"/message_to_raspPi\", message_to_TD)\n",
    "        time.sleep(45)\n",
    "        state = 0\n",
    "        client_TD.send_message(\"/state\", state)\n",
    "        \n",
    "    if keyboard.is_pressed('q'):\n",
    "        print('You pressed quit key!')\n",
    "        break  # finishing the loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n"
     ]
    }
   ],
   "source": [
    "# client.send_message(\"/messageIn\", message)\n",
    "print(message_to_TD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.send_message(\"/messageIn\", message_to_TD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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